刘涛,胡奎,张照喜,陈浩,贺瑶瑶,廖甜,李宁,刘明飞,袁子龙.体模实验观察体素大小对CT影像组学特征的影响[J].中国医学影像技术,2019,35(7):1099~1102
体模实验观察体素大小对CT影像组学特征的影响
Impact of voxel size on CT radiomics features: A phantom study
投稿时间:2018-08-23  修订日期:2018-11-26
DOI:10.13929/j.1003-3289.201808154
中文关键词:  体模,显象术  影像组学  体素  体层摄影术,X线计算机
英文关键词:phantoms, imaging  radiomics  voxel  tomography, X-ray computed
基金项目:科技部国家重点专项计划项目(2016YFC0103400)。
作者单位E-mail
刘涛 湖北省肿瘤医院放射科, 湖北 武汉 430079  
胡奎 湖北省肿瘤医院放射科, 湖北 武汉 430079  
张照喜 湖北省肿瘤医院放射科, 湖北 武汉 430079  
陈浩 湖北省肿瘤医院放射科, 湖北 武汉 430079  
贺瑶瑶 湖北省肿瘤医院放射科, 湖北 武汉 430079  
廖甜 湖北省肿瘤医院放射科, 湖北 武汉 430079  
李宁 湖北省肿瘤医院放射科, 湖北 武汉 430079  
刘明飞 湖北省肿瘤医院放射科, 湖北 武汉 430079  
袁子龙 湖北省肿瘤医院放射科, 湖北 武汉 430079 yuanzilong0213@126.com 
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中文摘要:
      目的 探讨体素大小对CT影像组学特征的影响。方法 采用Siemens Definition AS+ 64排CT扫描仪对美国体模实验室Catphan 700体模进行扫描,对扫描图像进行不同FOV和不同层厚的组合重建,2组图像体素大小范围分别为0.24~2.38 mm3和0.72~2.32 mm3。以3D Slicer软件手动勾画ROI,各计算提取7类共计108个特征,包括形状、一阶、灰度相关矩阵、灰度游程矩阵、灰度共生矩阵、灰度区域矩阵及邻域灰度差分矩阵特征,计算变异系数(CV)以评价不同FOV和层厚导致的体素改变对CT影像组学特征的影响。结果 体素改变对形状特征的影响较小(CV≤10%),而对其他6类特征中绝大部分的特征影响较大(CV>20%),其中依赖熵、短游程强调、游程熵、反差矩归一化、反差归一化、反差矩、反差及区域熵较稳定(CV均≤10%)。结论 改变体素大小对CT影像组学特征有较大影响,数据预处理可能是保证特征稳定性的较好途径,特别是对于多中心数据的应用及对比。
英文摘要:
      Objective To investigate the impact of voxel on CT radiomics features. Methods Catphan 700 phantom was used to perform CT scanning with Siemens definition AS+ 64 row CT scanner on head protocol. The images were reconstructed with different FOV and different thickness, so the ranges of voxel size in these two groups were 0.24-2.38 mm3 and 0.72-2.32 mm3, respectively. ROI was manually sketched using 3D Slicer software, and 108 features were extracted from 7 categories, including shape, first order, gray level dependence matrix, gray level run length matrix, gray level co-occurrence matrix, gray level size zone matrix and neighborhood gray-tone difference matrix. The coefficient of variation (CV) was adapted to evaluate the impact of voxel derived from FOV and slice thickness on CT radiomics features. Results The voxel had less effect on shape features (CV ≤ 10%), while it had a great impact on most features of other six feature groups (CV>20%). Among them, the dependence entropy, short run emphasis, run entropy, idmn, idn, idm, id and zone entropy were stable (all CV ≤ 10%). Conclusion The voxel size has a great impact on CT radiomics features. Data preprocessing may be a good way to ensure the stability of features, especially for the application and comparison of multi-centers data.
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